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The formatted source code for this file is here.
And a raw version here.
Previous work by Youngser Park can be found here.
We now have the following data sets:
featF0: The feature vector looking only at the integrated brightness features.fs: The feature vector scaled between \([0,1000]\).dat <- fs## Formatting data for heatmap
aggp <- apply(dat, 2, mean)
aggp <- t(cbind(aggp, aggp))[, ford]The following are heatmaps generated from clustering via K-means++ (at level 1)
heatmap.2(as.matrix(aggp),dendrogram='none',Colv=NA,trace="none",
col=mycol,colCol=ccol[ford],cexRow=0.8, keysize=1.25,symkey=FALSE,
symbreaks=FALSE,scale="none", srtCol=90,main="Heatmap of `fs` data.",
labRow = "") Percentage of data within cluster is presented on the right side of the heatmap.
set.seed(1024)
s2 <- sample(dim(dat)[1], 1e4)
ggJdat <- data.table(cbind(stack(dat[s2]),L[s2]))
ggJdat$ind <- as.factor(ggJdat$ind)
levels(ggJdat$ind) <- names(dat)[ford]
ggJ0 <-
ggplot(data = ggJdat, aes(x = ind, y = values)) +
geom_point(alpha=0.75) +
geom_jitter(width = 1) +
geom_boxplot(alpha =0.35, outlier.color = 'NA') +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(color = ccol[ford],
angle=45,
vjust = 0.5))print(ggJ0)The above scatter plot is a random sample of the data points.
cmatfs <- cor(fs)[ford, ford]
corrplot(cmatfs,method="color",tl.col=ccol[ford], tl.cex=1)pcaL0 <- prcomp(cmatfs, center=TRUE, scale=TRUE)pca0 <- data.frame(pcaL0$x)
rgl::plot3d(pca0[,1],pca0[,2],pca0[,3],type='s',col=ccol3[ford], size=1,
xlab = "PC1", ylab = "PC2", zlab = "PC3")
rgl::rgl.texts(pca0[,1],pca0[,2],pca0[,3],abbreviate(rownames(pca0)), col=ccol3[ford], adj=c(0,2))
title3d(main = "Lv 0")
subid <- currentSubscene3d()
rglwidget(elementId="rgl-pca0",width=720,height=720)Using LDA with re-substitution to create a voronoi diagram for Lv 0.
tr <- factor(exType, ordered = FALSE)
lda.fit0 <- lda(tr ~ ., data = pca0[, 1:10])
lda.pred0 <- predict(lda.fit0)
titlesvor <- paste("LDA decision boundaries for", paste0("F", 0:5))
voronoidf <- data.frame(lda.fit0$means)
#voronoidf <- data.frame(x=lda.fit$means[,1],y=lda.fit$means[,2])
#This creates the voronoi line segments
plot(pca0[,1:2], col=ccol3[ford], pch=20, cex=1.5, xlim =
c(min(pca0[,1:2]) -3, 5))
text(pca0[,1:2], labels=rownames(pca0),
pos=ifelse(exType %in% c('ex', 'in'), 2, 4),
col=ccol3[ford], cex=1.2)
deldir(x = voronoidf[,1],y = voronoidf[,2],
rw = c(-15,15,-15,15),
plotit=TRUE, add=TRUE, wl='te')
text(voronoidf[,1:2], labels=rownames(voronoidf), cex=1.25, pos=2)Staring from the correlation matrix cmatfs we compute the class means; \(v_1 = column\_mean(\)Excitatory\()\), \(v_2 = column\_mean(\)Inhibatory\()\), \(v_3 = column\_mean(\)Other\()\).
We then compute \(r_1 = v_1 - v_2\) and \(r_2 = v_1 - v_3\).
We then multiply \([cmatfs] \cdot [r_1 |\, r_2]\). The transformed points are then plotted below.
exMat <- cmatfs[exType == 'ex',] -> g1
inMat <- cmatfs[exType == 'in',] -> g2
otMat <- cmatfs[exType == 'other',] -> g3
mEx <- apply(exMat, 2, mean) -> v1
mIn <- apply(inMat, 2, mean) -> v2
mOt <- apply(otMat, 2, mean) -> v3
mXI <- mEx - mIn
mXO <- mEx - mOt
rotM <- data.frame(XI = mXI, XO = mXO) -> r1r2
colnames(r1r2) <- c("r1", "r2")
mdm2 <- data.frame(cmatfs %*% as.matrix(rotM))
mdm1 <- data.frame(XI = mdm2[,1], row.names=rownames(mdm2))
newFord <- order(mdm2$XI)heatmap.2(as.matrix(g1[, newFord]),dendrogram='none',
Colv=NA,trace="none",
col=mycol,colCol=ccol3[ford][newFord],
cexRow=0.8, keysize=1.25,
symkey=FALSE,symbreaks=FALSE,
scale="none", srtCol=90,
main="Excitatory") heatmap.2(as.matrix(g2[, newFord]),dendrogram='none',
Colv=NA,trace="none",
col=mycol,colCol=ccol3[ford][newFord],
cexRow=0.8, keysize=1.25,
symkey=FALSE,symbreaks=FALSE,
scale="none", srtCol=90,
main="Inhibatory") heatmap.2(as.matrix(g3[, newFord]),dendrogram='none',
Colv=NA,trace="none",
col=mycol,colCol=ccol3[ford][newFord],
cexRow=0.8, keysize=1.25,
symkey=FALSE,symbreaks=FALSE,
scale="none", srtCol=90,
main="Other") tmp <- t(cbind(v1, v1))[, newFord]
heatmap.2(as.matrix(tmp),dendrogram='none',
Colv=NA,trace="none", key= FALSE,
col=mycol,colCol=ccol3[ford][newFord],
cexRow=0.8, keysize=1.25,
symkey=FALSE,symbreaks=FALSE,
scale="none", srtCol=90, labRow='',
main="v1 = Mean Excitatory") tmp <- t(cbind(v2, v2))[, newFord]
heatmap.2(as.matrix(tmp),dendrogram='none',
Colv=NA,trace="none", key = FALSE,
col=mycol,colCol=ccol3[ford][newFord],
cexRow=0.8, keysize=1.25,
symkey=FALSE,symbreaks=FALSE,
scale="none", srtCol=90, labRow='',
main="v2 = Mean Inhibatory") tmp <- t(cbind(v3, v3))[, newFord]
heatmap.2(as.matrix(tmp),dendrogram='none',
Colv=NA,trace="none", key = FALSE,
col=mycol,colCol=ccol3[ford][newFord],
cexRow=0.8, keysize=1.25,
symkey=FALSE,symbreaks=FALSE,
scale="none", srtCol=90, labRow='',
main="v3 = Mean Other") rm(tmp)heatmap.2(as.matrix(t(r1r2)[, newFord]),dendrogram='none',
Colv=NA,trace="none",
col=mycol,colCol=ccol3[ford][newFord],
cexRow=0.8, keysize=1.25,
symkey=FALSE,symbreaks=FALSE,
scale="none", srtCol=90,
main="r1r2") plot(mdm2, col = ccol3[ford], pch = 20, cex=1)
abline(h = 0, v = 0)
text(mdm2, labels=rownames(mdm2), col=ccol3[ford], cex = 0.75, pos=1)
lm.fit <- lm(mdm2[,2] ~ mdm2[,1])
rL <- list(a = lm.fit$coefficients[1],
b = lm.fit$coefficients[2])
L1 <- function(x){ rL$a + rL$b * x }
summary(lm.fit)
abline(rL$a,rL$b, col = 'darkorange', lty=2)
text(-0.1,L1(0.1) , labels=expression(L[1]), col='darkorange', cex = 1)
legend(list(x= -1.5,y=1.6), legend = c('regression line'), col = 'darkorange', lty=2)
title("Mean-difference projections of markers")#
# Call:
# lm(formula = mdm2[, 2] ~ mdm2[, 1])
#
# Residuals:
# Min 1Q Median 3Q Max
# -0.39388 -0.23497 0.01289 0.20515 0.39076
#
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 0.14481 0.05626 2.574 0.0173 *
# mdm2[, 1] 0.72839 0.05723 12.726 1.28e-11 ***
# ---
# Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#
# Residual standard error: 0.2636 on 22 degrees of freedom
# Multiple R-squared: 0.8804, Adjusted R-squared: 0.875
# F-statistic: 162 on 1 and 22 DF, p-value: 1.276e-11
The following is the projection onto \(r_1\).
set.seed(2^8)
print(ggplot(mdm1, aes(x = XI, y = 0, label=rownames(mdm1))) +
geom_point(color = ccol3[ford]) +
geom_text(color = ccol3[ford], check_overlap=FALSE,
position='jitter'))We will now project the individual synapse points onto the
line \(L_1\) by first projecting into the mean-difference space then projecting onto \(L_1\) followed by a rotation into \(\mathbb{R}\).
mfs <- as.matrix(fs)
r <- as.matrix(mdm1)
projR1 <- mfs %*% r
plot(density(projR1))
if(FALSE){
require(ggvis)
data.frame(projR1) %>% ggvis(x = ~XI) %>%
layer_densities(
adjust = input_slider(.1, 2, value = 1, step = .1, label = "Bandwidth adjustment"),
kernel = input_select(
c("Gaussian" = "gaussian",
"Epanechnikov" = "epanechnikov",
"Rectangular" = "rectangular",
"Triangular" = "triangular",
"Biweight" = "biweight",
"Cosine" = "cosine",
"Optcosine" = "optcosine"),
label = "Kernel")
)
}svdfs <- svd(as.matrix(fs)[,newFord])
rightSV <- data.frame(t(svdfs$v[,1:2]))
colnames(rightSV) <- rownames(mdm2[order(mdm2$XI),])
heatmap.2(as.matrix(rightSV),dendrogram='row',Colv=NA,trace="none", col=mycol,colCol=ccol3[ford][newFord],cexRow=0.8, keysize=1.25,symkey=FALSE,symbreaks=FALSE,scale="none", srtCol=90,main="r. singular vectors of 'fs' data") We run a Hierachical K-means++ for \(K=2\) on the fs data with 4 levels.
set.seed(2^13)
L <- bhkmpp(dat,blevels=4)## Formatting data for heatmap
aggp <- aggregate(dat,by=list(lab=L[[1]]),FUN=mean)
aggp <- as.matrix(aggp[,-1])[, ford]
rownames(aggp) <- clusterFraction(L[[1]])The following are heatmaps generated from clustering via K-means++ (at level 1)
heatmap.2(as.matrix(aggp),dendrogram='row',Colv=NA,trace="none", col=mycol,colCol=ccol[ford],cexRow=0.8, keysize=1.25,symkey=FALSE,symbreaks=FALSE,scale="none", srtCol=90,main="Heatmap of `fs` data.") Percentage of data within cluster is presented on the right side of the heatmap.
ggCol <- brewer.pal(4,"Set1")[order(table(L[[1]]))]
cf1 <- data.frame(cf = clusterFraction(L[[1]]))
ggJ1 <-
ggplot(data = ggJdat, aes(x = ind, y = values,
color = as.factor(lv1))) +
scale_color_manual(values=ggCol, name="Cluster") +
geom_point(alpha=0.25, position=position_jitterdodge()) +
geom_boxplot(alpha =0.35, outlier.color = 'NA') +
annotate("text", x = levels(ggJdat$ind)[c(2,20)], y = 1.15*max(ggJdat$values),
label= cf1[1:2,]) +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(color = ccol[ford],
angle=45,
vjust = 0.5))print(ggJ1)corkp1 <- cor(dat[L[[1]] == 1,])[ford, ford]
corkp2 <- cor(dat[L[[1]] == 2,])[ford, ford]
difCor12 <- (corkp1 - corkp2)
layout(matrix(c(1,2,3,3), 2, 2, byrow=TRUE))
corrplot(corkp1,method="color",tl.col=ccol[ford], tl.cex=0.8, mar=c(0,0,3,0))
title("Cluster 1")
corrplot(corkp2,method="color",tl.col=ccol[ford], tl.cex=0.8, mar=c(0,0,3,0))
title("Cluster 2")
corrplot(difCor12,is.corr=FALSE,method="color",
tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0),
col=colorRampPalette(c("#998ec3","white","darkorange"))(50))
title("Difference(1,2)")Notice that the non-synaptic markers change very little between clusters. Also note that the correlations between (gad, VGAT, PV, Gephyr) and VGlut1 at both times change significantly between clusters.
Next we compute PCA for the within cluster correlation matrices and embed in 3d.
pcaL <- lapply(list(corkp1, corkp2), prcomp, center=TRUE, scale=TRUE)
elB <- lapply(pcaL, function(x) {getElbows(x$sdev, plot=FALSE)})pca <- pcaL[[1]]$x
rgl::plot3d(pca[,1],pca[,2],pca[,3],type='s',col=ccol3[ford], size=1,
xlab = "PC1", ylab = "PC2", zlab = "PC3")
rgl::rgl.texts(pca[,1],pca[,2],pca[,3],abbreviate(rownames(pca)), col=ccol3[ford], adj=c(0,2))
title3d(main = "Cluster 1: Lv 1")
subid <- currentSubscene3d()
rglwidget(elementId="rgl-pca1",width=720,height=720)pca <- pcaL[[2]]$x
rgl::plot3d(pca[,1],pca[,2],pca[,3],type='s',col=ccol3[ford], size=1,
xlab = "PC1", ylab = "PC2", zlab = "PC3")
rgl::rgl.texts(pca[,1],pca[,2],pca[,3],abbreviate(rownames(pca)), col=ccol3[ford], adj=c(0,2))
title3d(main = "Cluster 2: Lv 1")
subid <- currentSubscene3d()
rglwidget(elementId="rgl-pca2",width=720,height=720)lda.fit <-
lapply(list(pcaL[[1]]$x[,1:elB[[1]][2]],
pcaL[[2]]$x[,1:elB[[2]][2]]),
function(y) {
lda(tr ~ ., data = as.data.frame(y))
})
titlesvor <- paste("LDA decision boundaries for", paste0("C", 1:2))
voronoidf <- lapply(lapply(lda.fit, '[[', 3), data.frame)
#This creates the voronoi line segments
par(mfrow = c(1,2))
for(i in 1:length(pcaL)){
plot(pcaL[[i]]$x[,1:2], col=ccol3[ford], pch=20, cex=1.5)
title(titlesvor[i])
text(pcaL[[i]]$x[,1:2], labels=rownames(pcaL[[i]]$x),
pos=ifelse(pcaL[[i]]$x[,1]<max(pcaL[[i]]$x[,1] -0.5),4,2),
col=ccol3[ford], cex=1.2)
deldir(x = voronoidf[[i]][,1],y = voronoidf[[i]][,2], rw = c(-15,15,-15,15),
plotit=TRUE, add=TRUE, wl='te')
text(voronoidf[[i]], labels=rownames(voronoidf[[i]]), cex=1.5, pos=1)
}Using the location data and the results of K-means++ we show a 3d scatter plot colored accoding to cluster.
set.seed(2^12)
s1 <- sample(dim(loc)[1],5e4)
locs1 <- loc[s1,]
locs1$cluster <- L[[1]][s1]
plot3d(locs1$V1,locs1$V2,locs1$V3,
col=brewer.pal(4,"Set1")[order(table(L[[1]]))][locs1$cluster],
alpha=0.75,
xlab='x',
ylab='y',
zlab='z')
subid <- currentSubscene3d()
rglwidget(elementId="plot3dLocations", height=720, width=720)## Formatting data for heatmap
aggp2 <- aggregate(dat,by=list(lab=L[[2]]),FUN=function(x){mean(x)})
aggp2 <- as.matrix(aggp2[,-1])[, ford]
rownames(aggp2) <- clusterFraction(L[[2]])The following are heatmaps generated from clustering via K-means++
heatmap.2(as.matrix(aggp2),dendrogram='row',Colv=NA,trace="none", col=mycol,colCol=ccol[ford],cexRow=0.8, keysize=1.25,symkey=FALSE,symbreaks=FALSE,scale="none", srtCol=90,main="Heatmap of `fs` data.") Percentage of data within cluster is presented on the right side of the heatmap.
ggCol <- brewer.pal(8,"Set1")[order(table(L[[2]]))]
cf2 <- data.frame(cf = clusterFraction(L[[2]]))
ggJ2 <-
ggplot(data = ggJdat, aes(x = ind, y = values,
color = as.factor(lv2))) +
scale_color_manual(values=ggCol, name="Cluster") +
geom_point(alpha=0.25, position=position_jitterdodge()) +
geom_boxplot(alpha =0.35, outlier.color = 'NA') +
annotate("text", x = levels(ggJdat$ind)[c(2,8,14,20)], y = 1.15*max(ggJdat$values),
label= cf2[1:4,]) +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(color = ccol[ford],
angle=45,
vjust = 0.5))print(ggJ2)The fraction of data points within each cluster are given at the top of the plot window.
corLV2 <- lapply(c(1:4),function(x){cor(dat[L[[2]] == x,])[ford, ford]})
difCor1112 <- ((corLV2[[1]] - corLV2[[2]]))
difCor2122 <- ((corLV2[[3]] - corLV2[[4]]))
layout(matrix(c(1,2,3,3,4,5,6,6), 4, 2, byrow=TRUE))
corrplot(corLV2[[1]],method="color",tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0))
title("Cluster 1")
corrplot(corLV2[[2]],method="color",tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0))
title("Cluster 2")
corrplot(difCor1112, method="color", tl.col=ccol[ford],
tl.cex=0.8,
mar = c(0,0,3,0),
cl.lim = c(min(difCor1112,difCor2122),max(difCor1112,difCor2122)),
col=colorRampPalette(c("#998ec3",
"white",
"darkorange"))(100))
title("Difference(1,2)")
corrplot(corLV2[[3]],method="color",tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0))
title("Cluster 3")
corrplot(corLV2[[4]],method="color",tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0))
title("Cluster 4")
corrplot(difCor2122, method="color", tl.col=ccol[ford],
tl.cex=0.8,
mar=c(0,0,3,0),
cl.lim = c(min(difCor1112,difCor2122),max(difCor1112,difCor2122)),
col=colorRampPalette(c("#998ec3",
"white",
"darkorange"))(100))
title("Difference(3,4)")pcaL <- lapply(corLV2, prcomp, center=TRUE, scale=TRUE)
elB <- lapply(pcaL, function(x) {getElbows(x$sdev, plot=FALSE)})
pcaLel2 <- mapply(function(x,y){ x$x[,1:y[2]] }, pcaL, elB)lda.fit <-
lapply(pcaLel2,
function(y) {
lda(tr ~ ., data = as.data.frame(y))
})
titlesvor <- paste("LDA decision boundaries for", paste0("C", 1:4))
voronoidf <- lapply(lapply(lda.fit, '[[', 3), data.frame)
#This creates the voronoi line segments
par(mfrow = c(2,2))
for(i in 1:length(pcaL)){
plot(pcaL[[i]]$x[,1:2], col=ccol3[ford], pch=20, cex=1.5)
title(titlesvor[i])
text(pcaL[[i]]$x[,1:2], labels=rownames(pcaL[[i]]$x),
pos=ifelse(pcaL[[i]]$x[,1]<max(pcaL[[i]]$x[,1] -0.5),4,2),
col=ccol3[ford], cex=1.2)
deldir(x = voronoidf[[i]][,1],y = voronoidf[[i]][,2], rw = c(-15,15,-15,15),
plotit=TRUE, add=TRUE, wl='te')
text(voronoidf[[i]], labels=rownames(voronoidf[[i]]), cex=1.5, pos=1)
}Using the location data and the results of K-means++ we show a 3d scatter plot colored according to cluster.
set.seed(2^12)
s1 <- sample(dim(loc)[1],5e4)
locs2 <- loc[s1,]
locs2$cluster <- L[[2]][s1]
YlOrBr <- c("#FFFFD4", "#FED98E", "#FE9929", "#D95F0E", "#993404")
col.pal <- colorRampPalette(YlOrBr)
plot3d(locs2$V1,locs2$V2,locs2$V3,
#col=colorpanel(8,"brown","blue")[order(table(L[[2]]))][locs2$cluster],
col=col.pal(8)[-seq(1,8,2)][order(table(L[[2]]))][locs2$cluster],
alpha=0.75,
xlab='x',
ylab='y',
zlab='z'
)
subid <- currentSubscene3d()
rglwidget(elementId="plot3dLocationsLV2", height=720, width=720)## Formatting data for heatmap
aggp3 <- aggregate(dat,by=list(lab=L[[3]]),FUN=function(x){mean(x)})
aggp3 <- as.matrix(aggp3[,-1])[, ford]
rownames(aggp3) <- clusterFraction(L[[3]])The following are heatmaps generated from clustering via K-means++
heatmap.2(as.matrix(aggp3),dendrogram='row',Colv=NA,trace="none", col=mycol,colCol=ccol[ford],cexRow=0.8, keysize=1.25,symkey=FALSE,symbreaks=FALSE,scale="none", srtCol=90,main="Heatmap of `fs` data.") Percentage of data within cluster is presented on the right side of the heatmap.
ggCol <- brewer.pal(8,"Set1")[order(table(L[[3]]))]
cf3 <- data.frame(cf = clusterFraction(L[[3]]))
ggJ3 <-
ggplot(data = ggJdat, aes(x = ind, y = values,
color = as.factor(lv3))) +
scale_color_manual(values=ggCol, name="Cluster") +
geom_point(alpha=0.25, position=position_jitterdodge()) +
geom_boxplot(alpha =0.35, outlier.color = 'NA') +
annotate("text", x = levels(ggJdat$ind)[seq(2,22,length=8)], y = 1.05*max(ggJdat$values),
label= cf3[1:8,]) +
#geom_jitter(width=2) +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(color = ccol[ford],
angle=45,
vjust = 0.5))print(ggJ3)corLV3 <- lapply(c(1:8),function(x){cor(dat[L[[3]] == x,])[ford, ford]})
difCor1 <- (corLV3[[1]] - corLV3[[2]])
difCor2 <- (corLV3[[3]] - corLV3[[4]])
difCor3 <- (corLV3[[5]] - corLV3[[6]])
difCor4 <- (corLV3[[7]] - corLV3[[8]])
M <- max(difCor1, difCor2, difCor3, difCor4)
m <- min(difCor1, difCor2, difCor3, difCor4)
layout(matrix(c(1, 2, 3, 3,
4, 5, 6, 6,
7, 8, 9, 9,
10, 11, 12, 12), 8,2, byrow=TRUE))
corrplot(corLV3[[1]],method="color",tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0))
title('Cluster 1')
corrplot(corLV3[[2]],method="color",tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0))
title('Cluster 2')
corrplot(difCor1,method="color",tl.col=ccol[ford], tl.cex=0.8,
cl.lim=c(m,M),
mar=c(0,0,3,0),
col=colorRampPalette(c("#998ec3",
"white",
"darkorange"))(50))
title('Difference(1,2)')
corrplot(corLV3[[3]],method="color",tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0))
title('Cluster 3')
corrplot(corLV3[[4]],method="color",tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0))
title('Cluster 4')
corrplot(difCor2,method="color",tl.col=ccol[ford], tl.cex=0.8,
cl.lim= c(m,M),
mar=c(0,0,3,0),
col=colorRampPalette(c("#998ec3",
"white",
"darkorange"))(50))
title('Difference(3,4)')
corrplot(corLV3[[5]],method="color",tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0))
title('Cluster 5')
corrplot(corLV3[[6]],method="color",tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0))
title('Cluster 6')
corrplot(difCor3,method="color",tl.col=ccol[ford], tl.cex=0.8,
cl.lim= c(m,M),
mar=c(0,0,3,0),
col=colorRampPalette(c("#998ec3",
"white",
"darkorange"))(50))
title('Difference(5,6)')
corrplot(corLV3[[7]],method="color",tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0))
title('Cluster 7')
corrplot(corLV3[[8]],method="color",tl.col=ccol[ford], tl.cex=0.8,
mar=c(0,0,3,0))
title('Cluster 8')
corrplot(difCor4,method="color",tl.col=ccol[ford], tl.cex=0.8,
cl.lim= c(m,M),
mar=c(0,0,3,0),
col=colorRampPalette(c("#998ec3",
"white",
"darkorange"))(50))
title('Difference(7,8)')pcaL <- lapply(corLV3, prcomp, center=TRUE, scale=TRUE)
elB <- lapply(pcaL, function(x) {getElbows(x$sdev, plot=FALSE)})
pcaLel3 <- mapply(function(x,y){ x$x[,1:y[2]] }, pcaL, elB)lda.fit <-
lapply(pcaLel3,
function(y) {
lda(tr ~ ., data = as.data.frame(y))
})
titlesvor <- paste("LDA decision boundaries for", paste0("C", 1:8))
voronoidf <- lapply(lapply(lda.fit, '[[', 3), data.frame)
#This creates the voronoi line segments
par(mfrow = c(4,2))
for(i in 1:length(pcaL)){
plot(pcaL[[i]]$x[,1:2], col=ccol3[ford], pch=20, cex=1.5)
title(titlesvor[i])
text(pcaL[[i]]$x[,1:2], labels=rownames(pcaL[[i]]$x),
pos=ifelse(pcaL[[i]]$x[,1]<max(pcaL[[i]]$x[,1] -0.5),4,2),
col=ccol3[ford], cex=1.2)
deldir(x = voronoidf[[i]][,1],y = voronoidf[[i]][,2], rw = c(-15,15,-15,15),
plotit=TRUE, add=TRUE, wl='te')
text(voronoidf[[i]], labels=rownames(voronoidf[[i]]), cex=1.5, pos=1)
}Using the location data and the results of K-means++ we show a 3d scatter plot colored according to cluster.
set.seed(2^12)
s1 <- sample(dim(loc)[1],5e4)
locs3 <- loc[s1,]
locs3$cluster <- L[[3]][s1]
plot3d(locs3$V1,locs3$V2,locs3$V3,
col=col.pal(16)[-seq(1,8,2)][order(table(L[[3]]))][locs3$cluster],
alpha=0.65,
xlab='x',
ylab='y',
zlab='z'
)
subid <- currentSubscene3d()
rglwidget(elementId="plot3dLocationsLV3", height=720, width=720)